Deep embedded clustering of chlorophyll-a reveals distinct seasonal regimes in the Beibu Gulf, China

The concentration of chlorophyll-a (Chl-a) in nearshore waters is a pivotal indicator of marine ecosystem health and productivity. However, the spatial heterogeneity of Chl-a across coastal–offshore gradients and the dominant driving mechanisms at sub-regional scales remain poorly quantified, partly due to the reliance on predefined geomorphological boundaries. This study aims to objectively partition the Beibu Gulf (Guangxi) into functionally homogeneous sub-regions and to clarify the spatiotemporal variability and driving factors of Chl-a within each zone. MODIS-derived Chl-a data, combined with in situ observations, were used to conduct regionalization based on Deep Embedded Clustering (DEC), enabling data-driven delineation of On-shore, Mid-shore, and Off-shore areas. The spatiotemporal patterns and seasonal dynamics of Chl-a in each partition were analyzed using the Mann–Kendall trend test and global spatial autocorrelation (Moran’s I), while a geographic detector model and a self-organizing map (SOM) neural network were applied to identify dominant environmental drivers and their nonlinear influences. The results demonstrate that DEC-derived sub-regions exhibit higher internal similarity and lower dispersion of Chl-a variability in nearshore waters compared with conventional contour-line and buffer-zone divisions, supporting the ecological rationality of the proposed functional zonation. Distinct sub-regional phenological patterns were observed, with stronger seasonal and interannual variability nearshore and more stable but differentiated regimes offshore. The DEC-derived On-shore region shows a much lower Chl-a coefficient of variation (CV = 0.292) than contour line (CV = 0.881) and buffer zone (CV = 0.668) divisions, indicating higher internal homogeneity. Driver analysis reveals nearshore Chl-a variability is closely linked to terrestrial factors (wetlands, urban areas), while offshore dynamics are primarily driven by climatic and oceanographic processes, with sea surface salinity (q = 0.641) and temperature (q = 0.587) as dominant drivers, where q quantifies each factor’s contribution to Chl-a variance (0-1 scale). Overall, the results demonstrate that Chl-a spatial heterogeneity in the Beibu Gulf arises from the coupled effects of terrestrial inputs and climate-driven processes, and that DEC-based functional regionalization provides a more robust framework for interpreting Chl-a dynamics, offering a scientific basis for zoned marine management and climate-change adaptation.

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Publication Details

Journal
Science of Remote Sensing
Published
2026-03-12
DOI
https://doi.org/10.1016/j.srs.2026.100409
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
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article

Deep embedded clustering of chlorophyll-a reveals distinct seasonal regimes in the Beibu Gulf, China

Zhaohui Feng, Wenlu Lan, Yan Deng, Liusheng Han et al.
Science of Remote Sensing
Marine and coastal ecosystems
article

Deep embedded clustering of chlorophyll-a reveals distinct seasonal regimes in the Beibu Gulf, China

Zhaohui Feng, Wenlu Lan, Yan Deng, Liusheng Han, Ji Yang, Xiaoling Yin, Qian Xu, Wenlong Jing, Jia Sun, Hongda Hu, Xiaoyan Peng
article en

Abstract

The concentration of chlorophyll-a (Chl-a) in nearshore waters is a pivotal indicator of marine ecosystem health and productivity. However, the spatial heterogeneity of Chl-a across coastal–offshore gradients and the dominant driving mechanisms at sub-regional scales remain poorly quantified, partly due to the reliance on predefined geomorphological boundaries. This study aims to objectively partition the Beibu Gulf (Guangxi) into functionally homogeneous sub-regions and to clarify the spatiotemporal variability and driving factors of Chl-a within each zone. MODIS-derived Chl-a data, combined with in situ observations, were used to conduct regionalization based on Deep Embedded Clustering (DEC), enabling data-driven delineation of On-shore, Mid-shore, and Off-shore areas. The spatiotemporal patterns and seasonal dynamics of Chl-a in each partition were analyzed using the Mann–Kendall trend test and global spatial autocorrelation (Moran’s I), while a geographic detector model and a self-organizing map (SOM) neural network were applied to identify dominant environmental drivers and their nonlinear influences. The results demonstrate that DEC-derived sub-regions exhibit higher internal similarity and lower dispersion of Chl-a variability in nearshore waters compared with conventional contour-line and buffer-zone divisions, supporting the ecological rationality of the proposed functional zonation. Distinct sub-regional phenological patterns were observed, with stronger seasonal and interannual variability nearshore and more stable but differentiated regimes offshore. The DEC-derived On-shore region shows a much lower Chl-a coefficient of variation (CV = 0.292) than contour line (CV = 0.881) and buffer zone (CV = 0.668) divisions, indicating higher internal homogeneity. Driver analysis reveals nearshore Chl-a variability is closely linked to terrestrial factors (wetlands, urban areas), while offshore dynamics are primarily driven by climatic and oceanographic processes, with sea surface salinity (q = 0.641) and temperature (q = 0.587) as dominant drivers, where q quantifies each factor’s contribution to Chl-a variance (0-1 scale). Overall, the results demonstrate that Chl-a spatial heterogeneity in the Beibu Gulf arises from the coupled effects of terrestrial inputs and climate-driven processes, and that DEC-based functional regionalization provides a more robust framework for interpreting Chl-a dynamics, offering a scientific basis for zoned marine management and climate-change adaptation.

Science of Remote SensingVol. 13
Shandong University of Technology (CN), Peking University (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Guangxi Mangrove Research Center (CN), Guangzhou Institute of Geography (CN)
Life below water
Openalex Percentile: Top 37%
Marine and coastal ecosystems
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